The Growing Heterogeneity in the Farm Sector and Its Implications*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The farm sector has moved from one that was very homogeneous to one with significant differences in size and/or orientation. The decline in the number of “average‐sized” farm and the growth in the number of large farms are due primarily to technological innovations that push operations producing commodities to grow as a means of capturing economies of size. The increase in the relative number of small farms is also due partially to technical advances that allow for the production of food goods with the desired quality attributes to be delivered to the appropriate market. This market is continually being differentiated due to demographic and income shifts. The growing heterogeneity in farm structure complicates the assessment and design of farm policy. The social policy objective of improving the livelihood of farmers and their families could be achieved through farm support and extension programs when the sector was homogeneous. The policy objective has shifted toward improving the competitiveness of the sector, but for which of its components? The trend toward greater heterogeneity is likely to continue and thus so will the internal and external support for any policies targeted toward the farm sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it